Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You track your web applications. You inventory your APIs. But is anybody monitoring your AI servers? Just last week research found that there were more than 175,000 exposed versions of Ollama, an AI server popular for self-hosting LLMs. Across enterprises, self-hosted model servers are being deployed on cloud VMs and GPU-backed instances to power copilots, internal automation, and experimental AI features.

A sales rep opened Glean—an AI-powered enterprise search platform that connects to your company's SaaS apps and lets anyone query across all of them in natural language—typed "Who are my top 10 customers?" and got a clean, formatted list pulled from Salesforce, cross-referenced with HubSpot, and confirmed against data sitting in Google Drive. They copy-pasted that list into a personal Gmail draft. No alerts fired. No policies triggered. No one noticed. This isn't a hypothetical.

You’re evaluating AI workload security tools and every demo looks the same. Vendor A shows you an AI-SPM dashboard. Vendor B shows you a nearly identical AI-SPM dashboard with slightly different branding. Vendor C shows you posture findings with an “AI workload” tag that wasn’t there last quarter.

Last Tuesday, a platform security engineer at a mid-size fintech company ran a routine audit on their production Kubernetes clusters. The audit surfaced three LangChain-based agents, two vLLM inference servers, and a Model Context Protocol (MCP) tool runtime. None had been reported by the development teams. None appeared in any security inventory. All had been running for weeks. One of the agents had been making outbound API calls to a third-party data enrichment service every four minutes.

When you need to use a proxy to keep your zero trust environment secure, it often comes with a cost: poor performance for your users. Soon after deploying a client proxy, security teams are generally slammed with support tickets from users frustrated with sluggish browser speed, slow file transfers, and video calls glitching at just the wrong moment. After a while, you start to chalk it up to the proxy — potentially blinding yourself to other issues affecting performance.

New research shows the cybersecurity workforce is undergoing a major shift as AI transforms security operations. While leaders remain deeply committed to the field, many are facing increasing burnout, evolving skill demands, and growing responsibility for governing AI-driven systems. The future cybersecurity leader will need to balance technical expertise with communication, business alignment, and AI oversight.

For many engineering teams, CI/CD security appears to be working. Static scans run automatically. Vulnerabilities are flagged. Security checks exist somewhere in the pipeline. Yet issues still surface after release. The reason is rarely the absence of tools. More often, it is the absence of structural enforcement across the build lifecycle. Security controls run inside the pipeline, but they do not always guarantee that the artifact being tested is the same artifact that ultimately reaches users.

AI and automation are embedded in daily work. Copilots draft content and pull in customer context. Agents triage tickets, update records, and trigger workflows across Slack, Salesforce, Jira, and GitHub. In engineering, this acceleration shows up in scripts, CI/CD pipelines, and infrastructure automation that depend on secrets to ship and operate software.